


'''simple docstring'''


import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCAmelCase_						:  List[str]				  =						logging.getLogger(__name__)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= np.argmax(SCREAMING_SNAKE_CASE__				, axis=1	)
				return np.sum(outputs == labels	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				with open(SCREAMING_SNAKE_CASE__				, encoding="""utf_8"""	) as f:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= csv.reader(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Dict							= []
								next(SCREAMING_SNAKE_CASE__	)  # skip the first line
								for line in tqdm(SCREAMING_SNAKE_CASE__	):
												output.append((""" """.join(line[1:5]	), line[5], line[6], int(line[-1]	) - 1)	)
				return output







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= []
				for dataset in encoded_datasets:
								_SCREAMING_SNAKE_CASE					:			Any							= len(SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			Tuple							= np.zeros((n_batch, 2, input_len)				, dtype=np.intaa	)
								_SCREAMING_SNAKE_CASE					:			Dict							= np.zeros((n_batch, 2)				, dtype=np.intaa	)
								_SCREAMING_SNAKE_CASE					:			Dict							= np.full((n_batch, 2, input_len)				, fill_value=-100				, dtype=np.intaa	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= np.zeros((n_batch,)				, dtype=np.intaa	)
								for (
								    i,
								    (story, conta, conta, mc_label),
								) in enumerate(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			Tuple							= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
												_SCREAMING_SNAKE_CASE					:			int							= [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
												_SCREAMING_SNAKE_CASE					:			int							= with_conta
												_SCREAMING_SNAKE_CASE					:			Tuple							= with_conta
												_SCREAMING_SNAKE_CASE					:			Tuple							= len(SCREAMING_SNAKE_CASE__	) - 1
												_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= len(SCREAMING_SNAKE_CASE__	) - 1
												_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= with_conta
												_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= with_conta
												_SCREAMING_SNAKE_CASE					:			str							= mc_label
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (input_ids, mc_token_ids, lm_labels, mc_labels)
								tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE__	) for t in all_inputs	)	)
				return tensor_datasets







def      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= argparse.ArgumentParser()
				parser.add_argument("""--model_name"""				, type=SCREAMING_SNAKE_CASE__				, default="""openai-gpt"""				, help="""pretrained model name"""	)
				parser.add_argument("""--do_train"""				, action="""store_true"""				, help="""Whether to run training."""	)
				parser.add_argument("""--do_eval"""				, action="""store_true"""				, help="""Whether to run eval on the dev set."""	)
				parser.add_argument(
				    """--output_dir"""				, default=SCREAMING_SNAKE_CASE__				, type=SCREAMING_SNAKE_CASE__				, required=SCREAMING_SNAKE_CASE__				, help="""The output directory where the model predictions and checkpoints will be written."""				, )
				parser.add_argument("""--train_dataset"""				, type=SCREAMING_SNAKE_CASE__				, default=""""""	)
				parser.add_argument("""--eval_dataset"""				, type=SCREAMING_SNAKE_CASE__				, default=""""""	)
				parser.add_argument("""--seed"""				, type=SCREAMING_SNAKE_CASE__				, default=42	)
				parser.add_argument("""--num_train_epochs"""				, type=SCREAMING_SNAKE_CASE__				, default=3	)
				parser.add_argument("""--train_batch_size"""				, type=SCREAMING_SNAKE_CASE__				, default=8	)
				parser.add_argument("""--eval_batch_size"""				, type=SCREAMING_SNAKE_CASE__				, default=16	)
				parser.add_argument("""--adam_epsilon"""				, default=1E-8				, type=SCREAMING_SNAKE_CASE__				, help="""Epsilon for Adam optimizer."""	)
				parser.add_argument("""--max_grad_norm"""				, type=SCREAMING_SNAKE_CASE__				, default=1	)
				parser.add_argument(
				    """--max_steps"""				, default=-1				, type=SCREAMING_SNAKE_CASE__				, help=(
				        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
				    )				, )
				parser.add_argument(
				    """--gradient_accumulation_steps"""				, type=SCREAMING_SNAKE_CASE__				, default=1				, help="""Number of updates steps to accumulate before                        performing a backward/update pass."""				, )
				parser.add_argument("""--learning_rate"""				, type=SCREAMING_SNAKE_CASE__				, default=6.25E-5	)
				parser.add_argument("""--warmup_steps"""				, default=0				, type=SCREAMING_SNAKE_CASE__				, help="""Linear warmup over warmup_steps."""	)
				parser.add_argument("""--lr_schedule"""				, type=SCREAMING_SNAKE_CASE__				, default="""warmup_linear"""	)
				parser.add_argument("""--weight_decay"""				, type=SCREAMING_SNAKE_CASE__				, default=0.0_1	)
				parser.add_argument("""--lm_coef"""				, type=SCREAMING_SNAKE_CASE__				, default=0.9	)
				parser.add_argument("""--n_valid"""				, type=SCREAMING_SNAKE_CASE__				, default=374	)

				parser.add_argument("""--server_ip"""				, type=SCREAMING_SNAKE_CASE__				, default=""""""				, help="""Can be used for distant debugging."""	)
				parser.add_argument("""--server_port"""				, type=SCREAMING_SNAKE_CASE__				, default=""""""				, help="""Can be used for distant debugging."""	)
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= parser.parse_args()
				print(SCREAMING_SNAKE_CASE__	)

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print("""Waiting for debugger attach"""	)
								ptvsd.enable_attach(address=(args.server_ip, args.server_port)				, redirect_output=SCREAMING_SNAKE_CASE__	)
								ptvsd.wait_for_attach()

				random.seed(args.seed	)
				np.random.seed(args.seed	)
				torch.manual_seed(args.seed	)
				torch.cuda.manual_seed_all(args.seed	)

				_SCREAMING_SNAKE_CASE					:			List[Any]							= torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""	)
				_SCREAMING_SNAKE_CASE					:			int							= torch.cuda.device_count()
				logger.info("""device: {}, n_gpu {}""".format(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)	)

				if not args.do_train and not args.do_eval:
								raise ValueError("""At least one of `do_train` or `do_eval` must be True."""	)

				if not os.path.exists(args.output_dir	):
								os.makedirs(args.output_dir	)

				# Load tokenizer and model
				# This loading functions also add new tokens and embeddings called `special tokens`
				# These new embeddings will be fine-tuned on the RocStories dataset
				_SCREAMING_SNAKE_CASE					:			Any							= ["""_start_""", """_delimiter_""", """_classify_"""]
				_SCREAMING_SNAKE_CASE					:			int							= OpenAIGPTTokenizer.from_pretrained(args.model_name	)
				tokenizer.add_tokens(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			str							= OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name	)
				model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE__	)	)
				model.to(SCREAMING_SNAKE_CASE__	)

				# Load and encode the datasets
				def tokenize_and_encode(SCREAMING_SNAKE_CASE__	):
								if isinstance(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):
												return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(SCREAMING_SNAKE_CASE__	)	)
								elif isinstance(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):
												return obj
								return [tokenize_and_encode(SCREAMING_SNAKE_CASE__	) for o in obj]

				logger.info("""Encoding dataset..."""	)
				_SCREAMING_SNAKE_CASE					:			int							= load_rocstories_dataset(args.train_dataset	)
				_SCREAMING_SNAKE_CASE					:			str							= load_rocstories_dataset(args.eval_dataset	)
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= (train_dataset, eval_dataset)
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= tokenize_and_encode(SCREAMING_SNAKE_CASE__	)

				# Compute the max input length for the Transformer
				_SCREAMING_SNAKE_CASE					:			int							= model.config.n_positions // 2 - 2
				_SCREAMING_SNAKE_CASE					:			Tuple							= max(
				    len(story[:max_length]	) + max(len(conta[:max_length]	)				, len(conta[:max_length]	)	) + 3
				    for dataset in encoded_datasets
				    for story, conta, conta, _ in dataset	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= min(SCREAMING_SNAKE_CASE__				, model.config.n_positions	)  # Max size of input for the pre-trained model

				# Prepare inputs tensors and dataloaders
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pre_process_datasets(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, *SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= tensor_datasets[0], tensor_datasets[1]

				_SCREAMING_SNAKE_CASE					:			str							= TensorDataset(*SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			List[Any]							= RandomSampler(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= DataLoader(SCREAMING_SNAKE_CASE__				, sampler=SCREAMING_SNAKE_CASE__				, batch_size=args.train_batch_size	)

				_SCREAMING_SNAKE_CASE					:			Dict							= TensorDataset(*SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= SequentialSampler(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			str							= DataLoader(SCREAMING_SNAKE_CASE__				, sampler=SCREAMING_SNAKE_CASE__				, batch_size=args.eval_batch_size	)

				# Prepare optimizer
				if args.do_train:
								if args.max_steps > 0:
												_SCREAMING_SNAKE_CASE					:			Dict							= args.max_steps
												_SCREAMING_SNAKE_CASE					:			Dict							= args.max_steps // (len(SCREAMING_SNAKE_CASE__	) // args.gradient_accumulation_steps) + 1
								else:
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= len(SCREAMING_SNAKE_CASE__	) // args.gradient_accumulation_steps * args.num_train_epochs

								_SCREAMING_SNAKE_CASE					:			str							= list(model.named_parameters()	)
								_SCREAMING_SNAKE_CASE					:			Any							= ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
								_SCREAMING_SNAKE_CASE					:			str							= [
								    {
								        """params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay	)],
								        """weight_decay""": args.weight_decay,
								    },
								    {"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay	)], """weight_decay""": 0.0},
								]
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= AdamW(SCREAMING_SNAKE_CASE__				, lr=args.learning_rate				, eps=args.adam_epsilon	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= get_linear_schedule_with_warmup(
								    SCREAMING_SNAKE_CASE__				, num_warmup_steps=args.warmup_steps				, num_training_steps=SCREAMING_SNAKE_CASE__	)

				if args.do_train:
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[int]							= 0, 0, None
								model.train()
								for _ in trange(int(args.num_train_epochs	)				, desc="""Epoch"""	):
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= 0
												_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0
												_SCREAMING_SNAKE_CASE					:			str							= tqdm(SCREAMING_SNAKE_CASE__				, desc="""Training"""	)
												for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
																_SCREAMING_SNAKE_CASE					:			str							= tuple(t.to(SCREAMING_SNAKE_CASE__	) for t in batch	)
																_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Any							= batch
																_SCREAMING_SNAKE_CASE					:			List[str]							= model(SCREAMING_SNAKE_CASE__				, mc_token_ids=SCREAMING_SNAKE_CASE__				, lm_labels=SCREAMING_SNAKE_CASE__				, mc_labels=SCREAMING_SNAKE_CASE__	)
																_SCREAMING_SNAKE_CASE					:			Any							= args.lm_coef * losses[0] + losses[1]
																loss.backward()
																optimizer.step()
																scheduler.step()
																optimizer.zero_grad()
																tr_loss += loss.item()
																_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (
																    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																)
																nb_tr_steps += 1
																_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """Training loss: {:.2e} lr: {:.2e}""".format(SCREAMING_SNAKE_CASE__				, scheduler.get_lr()[0]	)

    # Save a trained model
				if args.do_train:
								# Save a trained model, configuration and tokenizer
								_SCREAMING_SNAKE_CASE					:			int							= model.module if hasattr(SCREAMING_SNAKE_CASE__				, """module"""	) else model  # Only save the model itself

								# If we save using the predefined names, we can load using `from_pretrained`
								_SCREAMING_SNAKE_CASE					:			Tuple							= os.path.join(args.output_dir				, SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			List[str]							= os.path.join(args.output_dir				, SCREAMING_SNAKE_CASE__	)

								torch.save(model_to_save.state_dict()				, SCREAMING_SNAKE_CASE__	)
								model_to_save.config.to_json_file(SCREAMING_SNAKE_CASE__	)
								tokenizer.save_vocabulary(args.output_dir	)

								# Load a trained model and vocabulary that you have fine-tuned
								_SCREAMING_SNAKE_CASE					:			Any							= OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= OpenAIGPTTokenizer.from_pretrained(args.output_dir	)
								model.to(SCREAMING_SNAKE_CASE__	)

				if args.do_eval:
								model.eval()
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[int]							= 0, 0
								_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= 0, 0
								for batch in tqdm(SCREAMING_SNAKE_CASE__				, desc="""Evaluating"""	):
												_SCREAMING_SNAKE_CASE					:			Tuple							= tuple(t.to(SCREAMING_SNAKE_CASE__	) for t in batch	)
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			int							= batch
												with torch.no_grad():
																_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= model(
																    SCREAMING_SNAKE_CASE__				, mc_token_ids=SCREAMING_SNAKE_CASE__				, lm_labels=SCREAMING_SNAKE_CASE__				, mc_labels=SCREAMING_SNAKE_CASE__	)

												_SCREAMING_SNAKE_CASE					:			Any							= mc_logits.detach().cpu().numpy()
												_SCREAMING_SNAKE_CASE					:			Any							= mc_labels.to("""cpu"""	).numpy()
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= accuracy(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

												eval_loss += mc_loss.mean().item()
												eval_accuracy += tmp_eval_accuracy

												nb_eval_examples += input_ids.size(0	)
												nb_eval_steps += 1

								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= eval_loss / nb_eval_steps
								_SCREAMING_SNAKE_CASE					:			str							= eval_accuracy / nb_eval_examples
								_SCREAMING_SNAKE_CASE					:			Any							= tr_loss / nb_tr_steps if args.do_train else None
								_SCREAMING_SNAKE_CASE					:			str							= {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

								_SCREAMING_SNAKE_CASE					:			List[Any]							= os.path.join(args.output_dir				, """eval_results.txt"""	)
								with open(SCREAMING_SNAKE_CASE__				, """w"""	) as writer:
												logger.info("""***** Eval results *****"""	)
												for key in sorted(result.keys()	):
																logger.info("""  %s = %s"""				, SCREAMING_SNAKE_CASE__				, str(result[key]	)	)
																writer.write("""%s = %s\n""" % (key, str(result[key]	))	)


if __name__ == "__main__":
				main()








'''simple docstring'''


import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


UpperCAmelCase_						:  List[Any]				  =						'sshleifer/bart-tiny-random'
UpperCAmelCase_						:  Tuple				  =						'patrickvonplaten/t5-tiny-random'





@require_torch
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							@cached_property
							def UpperCAmelCase_      (       self    ):
											return AutoConfig.from_pretrained(__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.num_hidden_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			str							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   self.teacher_config.encoder_layers    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											with self.assertRaises(__snake_case    ):
															create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=__snake_case       ,   d=__snake_case    )




